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Grape Leaf Black Rot Detection Based on Super-Resolution Image Enhancement and Deep Learning

The disease spots on the grape leaves can be detected by using the image processing and deep learning methods. However, the accuracy and efficiency of the detection are still the challenges. The convolutional substrate information is fuzzy, and the detection results are not satisfactory if the disea...

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Autores principales: Zhu, Jiajun, Cheng, Man, Wang, Qifan, Yuan, Hongbo, Cai, Zhenjiang
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8277438/
https://www.ncbi.nlm.nih.gov/pubmed/34267773
http://dx.doi.org/10.3389/fpls.2021.695749
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author Zhu, Jiajun
Cheng, Man
Wang, Qifan
Yuan, Hongbo
Cai, Zhenjiang
author_facet Zhu, Jiajun
Cheng, Man
Wang, Qifan
Yuan, Hongbo
Cai, Zhenjiang
author_sort Zhu, Jiajun
collection PubMed
description The disease spots on the grape leaves can be detected by using the image processing and deep learning methods. However, the accuracy and efficiency of the detection are still the challenges. The convolutional substrate information is fuzzy, and the detection results are not satisfactory if the disease spot is relatively small. In particular, the detection will be difficult if the number of pixels of the spot is <32 × 32 in the image. In order to effectively address this problem, we present a super-resolution image enhancement and convolutional neural network-based algorithm for the detection of black rot on grape leaves. First, the original image is up-sampled and enhanced with local details using the bilinear interpolation. As a result, the number of pixels in the image increase. Then, the enhanced images are fed into the proposed YOLOv3-SPP network for detection. In the proposed network, the IOU (Intersection Over Union, IOU) in the original YOLOv3 network is replaced with GIOU (Generalized Intersection Over Union, GIOU). In addition, we also add the SPP (Spatial Pyramid Pooling, SPP) module to improve the detection performance of the network. Finally, the official pre-trained weights of YOLOv3 are used for fast convergence. The test set test_pv from the Plant Village and the test set test_orchard from the orchard field were used to evaluate the network performance. The results of test_pv show that the grape leaf black rot is detected by the YOLOv3-SPP with 95.79% detection accuracy and 94.52% detector recall, which is a 5.94% greater in terms of accuracy and 10.67% greater in terms of recall as compared to the original YOLOv3. The results of test_orchard show that the method proposed in this paper can be applied in field environment with 86.69% detection precision and 82.27% detector recall, and the accuracy and recall were improved to 94.05 and 93.26% if the images with the simple background. Therefore, the detection method proposed in this work effectively solves the detection task of small targets and improves the detection effectiveness of the grape leaf black rot.
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spelling pubmed-82774382021-07-14 Grape Leaf Black Rot Detection Based on Super-Resolution Image Enhancement and Deep Learning Zhu, Jiajun Cheng, Man Wang, Qifan Yuan, Hongbo Cai, Zhenjiang Front Plant Sci Plant Science The disease spots on the grape leaves can be detected by using the image processing and deep learning methods. However, the accuracy and efficiency of the detection are still the challenges. The convolutional substrate information is fuzzy, and the detection results are not satisfactory if the disease spot is relatively small. In particular, the detection will be difficult if the number of pixels of the spot is <32 × 32 in the image. In order to effectively address this problem, we present a super-resolution image enhancement and convolutional neural network-based algorithm for the detection of black rot on grape leaves. First, the original image is up-sampled and enhanced with local details using the bilinear interpolation. As a result, the number of pixels in the image increase. Then, the enhanced images are fed into the proposed YOLOv3-SPP network for detection. In the proposed network, the IOU (Intersection Over Union, IOU) in the original YOLOv3 network is replaced with GIOU (Generalized Intersection Over Union, GIOU). In addition, we also add the SPP (Spatial Pyramid Pooling, SPP) module to improve the detection performance of the network. Finally, the official pre-trained weights of YOLOv3 are used for fast convergence. The test set test_pv from the Plant Village and the test set test_orchard from the orchard field were used to evaluate the network performance. The results of test_pv show that the grape leaf black rot is detected by the YOLOv3-SPP with 95.79% detection accuracy and 94.52% detector recall, which is a 5.94% greater in terms of accuracy and 10.67% greater in terms of recall as compared to the original YOLOv3. The results of test_orchard show that the method proposed in this paper can be applied in field environment with 86.69% detection precision and 82.27% detector recall, and the accuracy and recall were improved to 94.05 and 93.26% if the images with the simple background. Therefore, the detection method proposed in this work effectively solves the detection task of small targets and improves the detection effectiveness of the grape leaf black rot. Frontiers Media S.A. 2021-06-29 /pmc/articles/PMC8277438/ /pubmed/34267773 http://dx.doi.org/10.3389/fpls.2021.695749 Text en Copyright © 2021 Zhu, Cheng, Wang, Yuan and Cai. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Plant Science
Zhu, Jiajun
Cheng, Man
Wang, Qifan
Yuan, Hongbo
Cai, Zhenjiang
Grape Leaf Black Rot Detection Based on Super-Resolution Image Enhancement and Deep Learning
title Grape Leaf Black Rot Detection Based on Super-Resolution Image Enhancement and Deep Learning
title_full Grape Leaf Black Rot Detection Based on Super-Resolution Image Enhancement and Deep Learning
title_fullStr Grape Leaf Black Rot Detection Based on Super-Resolution Image Enhancement and Deep Learning
title_full_unstemmed Grape Leaf Black Rot Detection Based on Super-Resolution Image Enhancement and Deep Learning
title_short Grape Leaf Black Rot Detection Based on Super-Resolution Image Enhancement and Deep Learning
title_sort grape leaf black rot detection based on super-resolution image enhancement and deep learning
topic Plant Science
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8277438/
https://www.ncbi.nlm.nih.gov/pubmed/34267773
http://dx.doi.org/10.3389/fpls.2021.695749
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AT wangqifan grapeleafblackrotdetectionbasedonsuperresolutionimageenhancementanddeeplearning
AT yuanhongbo grapeleafblackrotdetectionbasedonsuperresolutionimageenhancementanddeeplearning
AT caizhenjiang grapeleafblackrotdetectionbasedonsuperresolutionimageenhancementanddeeplearning